A few weeks ago, Mike Snyder gave the last lecture of Personalized Medicine and Genomics (Genetics 210) here at Stanford University, a course for which I’ve had the privilege of being a teaching assistant. In this pilot program, Stanford medical and graduate students were taught about the state-of-the-art in personal genetics and given the option to get themselves genotyped. While we are still analyzing course survey data and it has not yet been established if the course will be offered again (in its current form), one thing is clear: everyone involved in the course, from the students to the teachers, from the proponents to the critics, learned something about genetic testing. The San Francisco Chronicle did a great job covering the class before and after, but I thought I’d cover a bit more of the details.

33 students (the TA included) learned something about their personal genetic risks. Whether it was an genetically estimated risk of prostate cancer from the population average 17% to a personal 24% or an “increased-risk” designation for hypertension, these students now understand and can connect with the contribution of their genetics to their personal health. While the concept of an odds ratio could have been presented starting with statistics and ending with an anonymous number that confers a disease risk to an anonymous group of people, instructors Keyan Salari and Euan Ashley presented this concept to students with their own genetic reality, making the results of the students’ analysis real and tangible. When someone sees they have a TT genotype at a locus and the disease is more common in people who have a TT at the same locus (than others who may have an AT or AA genotype), it makes them think critically as to what this means in general, for the population, and to them. Importantly, the students now understand that a genetic test is not a diagnosis. It is a scientifically informed estimate of disease risk, based on the application of published scientific studies (which students were taught to scrutinize and analyze critically, with all the reasons a study like this may fail or at least be incomplete) to a personal genome. While there exist a few conditions for which genetics plays a majority role, most results provided by a DTC genetic testing company (such as 23andme) confer moderate risks. A typical result may involve an increase from a 1/6 chance of getting prostate cancer at some point in life to a 1/4 chance. At first glance, the suggestion that this person is “high risk” for a disease may sound scary to the uninformed. However, if a person were told the risk for prostate cancer for individuals of his race and ethnic background were 24% in his population (while only 16% overall), this would likely not cause undue stress, but he would be more informed and consider earlier screening options.

The same goes for the students response to various drugs. In a lecture on Pharmacogenomics, Russ Altman brought his expertise of gene-drug interactions and made it personal. It’s easy to say “different people respond to drugs differently based on genetics,” but it is not until one sees their own genetics suggesting an increased sensitivity to warfarin that one can pause and say “If I didn’t know my genetic factors for warfarin dosing, I might be prescribed too much (which can cause side effects such as hemorrhaging).” Even if this person is not currently taking warfarin, it is easy to discern which of these two statements are more effective in learning about pharmacogenomics.

These students also learned something about what their DNA can tell them about their ancestry. In Carlos Bustamante’s lecture, running PCA and admixture methods, they observed where they fell on the “genetic map.” Most of the time, this was not news to the students: after all, ancestry is not a particularly anonymous trait. However, seeing the power of these methods to detect differences between populations and separate even an individual’s genome into African, Asian, and European derived sections demonstrated the extent of diversity among individuals who are otherwise 99.93% similar. Of course, some students observed results that were not as straightforward and could only be explained by dissecting the methods employed, a personal foray into scientific analysis.

Along these lines, one particularly important lesson students learned was a scientific look at studies involving genetic information. For instance, students observed the shortcomings of genetic information, such as the inability to significantly predict a fairly heritable trait, height (although it should be noted here that the instructors learned a valuable lesson about science education, that not everything works out as planned; in the course of height prediction of this particular sample, genetics were better able to predict height than the prediction based on parents’ heights, the opposite of the typically reported result). In addition, with Stuart Kim’s perspective on the genomics of aging, the students explored the scientific methods behind these studies, through a closer look at the centenarian prediction paper, which was published while the class was in session and subsequently questioned by the scientific community.

The course ended with Mike Snyder’s vision on the future of personal genomics and what will happen when the cost of a full genome sequence falls to consumer-affordable levels. At this point, an individual may have knowledge about his or her rare variants or the rarest variants, “private” mutations, where annotation and information is not as readily available as studied and annotated SNPs. The students were encouraged to think about this largely unexplored area which will no doubt become integrated with broader aspects of biology and science.

Before the students decided whether to get genotyped or not, they were presented with perspectives (from genetic counselors and ethicists, Kelly Ormond, Louanne Hudgins, Hank Greely, and Mike Grecius) and asked to think critically about the implications of knowing their personal genotype. This was no doubt an important aspect of the course, providing informed consent to the medical and graduate students undertaking to receive information with which they may not have been familiar. Once they made their decision, however, the informing did not end there. The lectures and exercises (and options for genetic counseling) encouraged students to constantly explore their genetics, to truly understand the basis of the information and what it means to them. With students invested in the analysis of their personal genetic data, we hoped to effectively teach the personal nature of genetics. While we have not yet fully analyzed the final effects of the course and the effectiveness of a genotyping option, at the very least, we believe we have dispelled some of the fears and controversy around genetic testing for these students. And hopefully, the 60 students that took time out of their summer to take an optional elective course learned something about personal genotypes.

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5 Responses to “Stanford Personalized Medicine Course”

Hey Konrad
It sounds like a great course. I found this with a Daily Scan posting about interpretome.

I designed and rolled out a similar course here in Maine that I call Personalized Medicine and Bioinformatics. My first offering of the course was last August at MDIBL. The difference is that I taught my course to top-notch High School students. We did a lot of similar things. For ethical reasons we did CYP2C19 SNP genotyping sort of the pharmacogenomics…staying away from disease risk area. We also ran a segment on Bio-ethics as I caught someone from Ting Wu’s lab at Harvard to lead an ethics workshop.
It was hugely successful and I will doing it again this year. We do all our own lab work and I get the students using allele specific fluorescent PCR to type (anonymously) their SNPs. I do all of this myself without access to the great lectures you mention. I wish I had the horsepower that STanford seemed to use on the effort
I would like to talk to you sometime about your experience, how Stanford deals with Informed Consent: the future of the course, Interpretome, use of 23andMe paltform other DTC testing etc
CW